import torch.nn as nn
import torch
import math

from config import config

def resnet50(pretrained=False, **kwargs):
  """Constructs a ResNet-50 model.
  Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
  """
  model = ResNet([3, 4, 6, 3], **kwargs)
  if pretrained:
    model.load_state_dict(torch.load(model.modelPath))
  return model


def resnet101(pretrained=False, **kwargs):
  """Constructs a ResNet-101 model.
  Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
  """
  model = ResNet([3, 4, 23, 3], **kwargs)
  if pretrained:
    model.load_state_dict(torch.load(model.modelPath))
  return model


class ResNet(nn.Module):
  """
  block: A sub module
  """

  def __init__(self, layers, num_classes=2000, model_path=config.model + "resnet_50.nn"):
    super(ResNet, self).__init__()
    self.inplanes = 64
    self.modelPath = model_path
    self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                           bias=False)
    self.bn1 = nn.BatchNorm2d(64)
    self.relu = nn.ReLU(inplace=True)
    self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
    self.stack1 = self.make_stack(64, layers[0])
    self.stack2 = self.make_stack(128, layers[1], stride=2)
    self.stack3 = self.make_stack(256, layers[2], stride=2)
    self.stack4 = self.make_stack(512, layers[3], stride=2)
    self.avgpool = nn.AvgPool2d(7, stride=1)
    self.fc = nn.Linear(512 * Bottleneck.expansion, num_classes)
    # initialize parameters
    self.init_param()

  def init_param(self):
    # The following is initialization
    for m in self.modules():
      if isinstance(m, nn.Conv2d):
        n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
        m.weight.data.normal_(0, math.sqrt(2. / n))
      elif isinstance(m, nn.BatchNorm2d):
        m.weight.data.fill_(1)
        m.bias.data.zero_()
      elif isinstance(m, nn.Linear):
        n = m.weight.shape[0] * m.weight.shape[1]
        m.weight.data.normal_(0, math.sqrt(2. / n))
        m.bias.data.zero_()

  def make_stack(self, planes, blocks, stride=1):
    downsample = None
    layers = []

    if stride != 1 or self.inplanes != planes * Bottleneck.expansion:
      downsample = nn.Sequential(
        nn.Conv2d(self.inplanes, planes * Bottleneck.expansion,
                  kernel_size=1, stride=stride, bias=False),
        nn.BatchNorm2d(planes * Bottleneck.expansion),
      )

    layers.append(Bottleneck(self.inplanes, planes, stride, downsample))
    self.inplanes = planes * Bottleneck.expansion
    for i in range(1, blocks):
      layers.append(Bottleneck(self.inplanes, planes))

    return nn.Sequential(*layers)

  def forward(self, x):
    x = self.conv1(x)
    x = self.bn1(x)
    x = self.relu(x)
    x = self.maxpool(x)

    x = self.stack1(x)
    x = self.stack2(x)
    x = self.stack3(x)
    x = self.stack4(x)

    x = self.avgpool(x)
    x = x.view(x.size(0), -1)
    x = self.fc(x)

    return x


class Bottleneck(nn.Module):
  expansion = 4

  def __init__(self, inplanes, planes, stride=1, downsample=None):
    super(Bottleneck, self).__init__()
    self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
    self.bn1 = nn.BatchNorm2d(planes)
    self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
    self.bn2 = nn.BatchNorm2d(planes)
    self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
    self.bn3 = nn.BatchNorm2d(planes * 4)
    self.relu = nn.ReLU(inplace=True)
    self.downsample = downsample
    self.stride = stride

  def forward(self, x):
    residual = x

    out = self.conv1(x)
    out = self.bn1(out)
    out = self.relu(out)

    out = self.conv2(out)
    out = self.bn2(out)
    out = self.relu(out)

    out = self.conv3(out)
    out = self.bn3(out)

    if self.downsample is not None:
      residual = self.downsample(x)

    out += residual
    out = self.relu(out)

    return out

